Volatility stylized facts in the Moroccan stock market: Evidence from both aggregate and disaggregate data
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Keywords

Asymmetry
long memory
multifractality Omori Law
Stylized facts
Volatility.

How to Cite

ELBOUSTY, M. D., & OUBDI, L. (2020). Volatility stylized facts in the Moroccan stock market: Evidence from both aggregate and disaggregate data. Turkish Economic Review, 7(2), 111–138. https://doi.org/10.1453/ter.v7i2.2077

Abstract

Abstract. Financial markets in emerging countries are generating considerable literature, aiming to understand their organization, perspective, and performance. In this context, few studies have expressed interest in the Moroccan financial market and even fewer researches have addressed the issue of the Moroccan financial market volatility. In this paper, we investigate variety of common properties, labelled as “stylized facts. Our results show that global and sectoral indices of Moroccan Stock Market share the majority of stylized facts. In fact, absolute returns correlation coefficients are positive and tends to decay at a much slower pace. Hence, volatility of Moroccan Stock Market captures the properties of volatility clustering and long memory. We also find evidence of volatility asymmetry. Yet, the level is not statistically significant for most of the indices. More interestingly, the Omori law indicates that Moroccan Stock market is relatively stable after financial shocks.

Keywords. Asymmetry, long memory, multifractality Omori Law, Stylized facts, Volatility.

JEL. G11, G17, C53, C58.
https://doi.org/10.1453/ter.v7i2.2077
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References

Al-Hajieh, H. (2015). Behavioral of Islamic financial markets: The case of asymmetric behavioral of 17 countries. International Journal of Economics, Commerce and Management, 3(7), 1-15.

Alagidede, P. (2011). Return behaviour in Africa’s emerging equity markets. Quarterly Review of Economics and Finance, 51(2), 133-140. doi. 10.1016/j.qref.2011.01.004

Andersen, T.G., & Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets, Journal of Emorical Finance, 4(2-3), 115–158. doi. 10.1016/S0927-5398(97)00004-2

Anoruo, E., & Gil-alana, L.A. (2011). Mean reversion and long memory in African stock market prices. Journal of Economics and Finance, 35, 296–308. doi. 10.1007/s12197-010-9124-0

Assaf, A. (2015). MENA stock market volatility persistence: Evidence before and after the financial crisis of 2008. Research in International Business and Finance, 36, 222-240. doi. 10.1016/j.ribaf.2015.09.003

Bakir, K. (n.d.). L’efficience des marchés financiers des pays émergents : l’exemple de la bourse de Casablanca. [Retrieved from].

Banque de France. (2003). La volatilité boursière: des constats empiriques aux difficultés d’interprétation. [Retrieved from].

Benbachir, S., & El Alaoui, M. (2011). A multifractal detrended fluctuation analysis of the Moroccan Stock Exchange. MPRA Papers, No.8225. [Retrieved from].

Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654. doi. 10.1086/260062

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. While conventional time series and econometric models operate under an assumption of constant variance, the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982). 31, 307–327. [Retrieved from].

Bollerslev, T., & Mikkelsen, H. O. (1996). Modeling and pricing long memory in stock market volatility. Journal of Econometrics, 73(1), 151–184. doi. 10.1016/0304-4076(95)01736-4

Boubaker, A., & Makram, B. (2012). Modelling heavy tails and double long memory in North African stock market returns. Journal of North African Studies, 17(2), 195–214. doi. 10.1080/13629387.2012.655068

Brooks, R. (2007). Power arch modelling of the volatility of emerging equity markets. Emerging Markets Review, 8(2), 124-133. doi. 10.1016/j.ememar.2007.01.002

Coffie, W. (2017). Conditional heteroscedasticity and stock market returns: Empirical evidence from Morocco and BVRM. Journal of Applied Business and Economics, 19(5), 43-57.

Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223-236. doi. 10.1080/713665670

Daly, K. (2011). An overview of the determinants of financial volatility: An explanation of measuring techniques. Modern Applied Science, 5(5), 46–63. doi. 10.5539/mas.v5n5p46

Ederington, L., & Guan, W. (2006). Measuring historical volatility. Journal of Applied Finance, 16(1), 5-12.

El Bakkouchi, M. (2014). Analyse du risque de marché boursier marocain en période de crise des subprimes: Cas de l’indice MASI.

Elyasiani, E., Mansur, I., & Odusami, B. (2011). Oil price shocks and industry stock returns. Energy Economics, 33(5), 966-974. doi. 10.1016/j.eneco.2011.03.013

Figlewski, S. (2004). Forecasting volatility. [Retrieved from].

Geoffrey, T.K.A., & Darrat, A.F. (2017). Long Memory or Structural Breaks : Some Evidence for African Stock Markets. Review of Financial Economics, 34(1), 61-73. doi. 10.1016/j.rfe.2017.06.003

Geweke, J. (1986). Exact infrence in the inequality constrained noram linear regeression nodel. Journal of Applied Econometrics, 1(2), 127–141. doi. 10.1002/jae.3950010203

Lahmiri, S. (2017). On fractality and chaos in Moroccan family business stock returns and volatility. Physica A: Statistical Mechanics and Its Applications, 473, 29–39. doi. 10.1016/j.physa.2017.01.033

Lamoureux, C.G., & Lastrapes, W.D. (1990). Persistence in variance, struetura change, and the GARCH model, Journal of Business & Economic Statistics, 8(2), 225–234.

Lillo, F., & Mantegna, R.N. (2003). Power-law relaxation in a complex system: Omori law after a financial market crash. Physical Review E-Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 68(1), 5. doi. 10.1103/PhysRevE.68.016119

Limam, I. (2003). Is long memory a property of thin stock markets? International evidence using Arab countries. Review of Middle East Economics and Finance, 1(3), 251-266. doi. 10.1080/1475368032000158241

Liu, H., & Loewenstein, M. (2013). Market crashes, correlated illiquidity, and portfolio choice. Management Sciences, 59(3), 715-732. doi. 10.1287/mnsc.1120.1561

Masset, P. (2011). Volatility stylized facts. (September), 1–91.

Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347. doi. 10.2307/2938260

Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61. doi. 10.1086/296071

Petersen, A.M., Wang, F., Havlin, S., & Stanley, H.E. (2010). Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws. [Retrieved from].

Poon, S.H., & Granger, C. (2003). Forecasting financial market volatility: A review. Journal of Economic Literature, 41(2), 478–539. doi. 10.1257/002205103765762743

Selçuk, F. (2004). Financial earthquakes, aftershocks and scaling in emerging stock markets. Physica A: Statistical Mechanics and Its Applications, 333(1-4), 306-316. doi. 10.1016/j.physa.2003.10.060

Sornette, D., & Helmstetter, A. (2003). Endogenous versus exogenous shocks in systems with memory. Physica A: Statistical Mechanics and Its Applications, 318(3-4), 577-591. doi. 10.1016/S0378-4371(02)01371-7

Taylor, P., & Dieobold, F.X. (1986). Modeling the persistence of conditional variances: A comment. Econometric Reviews, 5(1), 51-56. doi. 10.1080/07474938608800096

Weber, P., Wang, F., Vodenska-Chitkushev, I., Havlin, S., & Stanley, H.E. (2007). Relation between volatility correlations in financial markets and Omori processes occurring on all scales. Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 76(1). doi. 10.1103/PhysRevE.76.016109

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